| Yazarlar (3) |
Dr. Öğr. Üyesi Fatih UYSAL
Kafkas Üniversitesi, Türkiye |
|
Fırat Hardalaç
Gazi Üniversitesi, Türkiye |
|
Mustafa Koç
Fırat Üniversitesi, Türkiye |
| Özet |
| Prostate cancer is a type of cancer that is very common in men. Literature review, it has been observed that there are many studies conducted on this prostate image using various image processing methods for cancer diagnosis and treatment. Secondary hemorrhage sites in prostate biopsy may cause misdiagnosis in T2-weighted magnetic resonance (MR) prostate images, in terms of tumor. In these cases, T1-weighted MR imaging of the prostate is helpful in diagnosing. In such situations, it may be helpful to prevent misdiagnosis and to help diagnosis; In this study, one deep convolutional neural network learning algorithms (CNN) using T1 and T2-weighted MR image classification process of the prostate were performed. As a result of this, an CNN model was developed that can classify MR prostate images. |
| Anahtar Kelimeler |
| convolutional neural networks | deep learning | image classification | magnetic resonance prostate images |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Ulusal Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Ulusal Kongre/Sempozyum |
| Bildiri Dili | Türkçe |
| Kongre Adı | Tıp Teknolojileri Ulusal Kongresi, TIPTEKNO’18 |
| Kongre Tarihi | 08-11-2018 / 10-11-2018 |
| Basıldığı Ülke | Kuzey Kıbrıs Türk Cumhuriyeti |
| Basıldığı Şehir | Gazi Magosa |
| Atıf Sayıları | |
| SCOPUS | 1 |
